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arXiv 2305.02470astro-ph.EPastro-ph.IMcs.AIcs.LG
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凌星信号分类器的多重性提升:利用 ExoMiner 的多重性提升验证 69 颗新系外行星

Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner

Universities Space Research Association(大学空间研究协会);NASA Ames Research Center(美国国家航空航天局艾姆斯研究中心);The SETI Institute(搜寻地外文明研究所)

Hamed Valizadegan, Miguel J. S. Martinho, Jon M. Jenkins, Douglas A. Caldwell, Joseph D. Twicken, Stephen T. Bryson

AI总结:

本文提出利用多重性信息提升凌星信号分类器性能的通用框架,并将其应用于 ExoMiner V1.2,最终从 Kepler 星表的多 KOI 系统中验证了 69 颗新系外行星。

摘要:

大多数现有系外行星的发现是通过验证技术而非补充观测来确认的。这些技术会生成一个分数,该分数通常是凌星信号为系外行星(y(x)=exoplanet)的概率,给定与该信号相关的一些信息(用 x 表示)。除了 Rowe 等人(2014)的验证技术利用多重性信息来生成这些概率分数之外,现有的验证技术都忽略了多重性提升信息。在这项工作中,我们引入了一个框架,其前提如下:给定一个现有的凌星信号审查器(分类器),利用多重性信息提升其性能。我们将该框架应用于多个现有分类器,包括 vespa(Morton 等人,2016)、Robovetter(Coughlin 等人,2017)、AstroNet(Shallue 和 Vanderburg,2018)、ExoNet(Ansdel 等人,2018)、GPC 和 RFC(Armstrong 等人,2020)以及 ExoMiner(Valizadegan 等人,2022),以支持我们的主张,即该框架能够提升给定分类器的性能。随后,我们将所提出的多重性提升框架用于 ExoMiner V1.2,该版本解决了原始 ExoMiner 分类器(Valizadegan 等人,2022)的一些不足,并为来自 Kepler 星表中具有多个 KOI 的系统验证了 69 颗新系外行星。

英文摘要:

Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations. These techniques generate a score that is typically the probability of the transit signal being an exoplanet (y(x)=exoplanet) given some information related to that signal (represented by x). Except for the validation technique in Rowe et al. (2014) that uses multiplicity information to generate these probability scores, the existing validation techniques ignore the multiplicity boost information. In this work, we introduce a framework with the following premise: given an existing transit signal vetter (classifier), improve its performance using multiplicity information. We apply this framework to several existing classifiers, which include vespa (Morton et al. 2016), Robovetter (Coughlin et al. 2017), AstroNet (Shallue & Vanderburg 2018), ExoNet (Ansdel et al. 2018), GPC and RFC (Armstrong et al. 2020), and ExoMiner (Valizadegan et al. 2022), to support our claim that this framework is able to improve the performance of a given classifier. We then use the proposed multiplicity boost framework for ExoMiner V1.2, which addresses some of the shortcomings of the original ExoMiner classifier (Valizadegan et al. 2022), and validate 69 new exoplanets for systems with multiple KOIs from the Kepler catalog.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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